Forecasting People's Needs in Hurricane Events from Social Network

Long Nguyen, Zhou Yang, Jia Li, Zhenhe Pan, Guofeng Cao, Fang Jin

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


Social networks can serve as a valuable communication channel for asking for help, offering assistance, and coordinating rescue activities in disaster because it allows users to continuously update critical information in the fast-changing disaster environment. This paper presents a novel sequence to sequence based framework for forecasting people's needs during disasters using social media and weather data. It consists of two Long Short-Term Memory (LSTM) models, one of which encodes input sequences of weather information and the other plays as a conditional decoder that decodes the encoded vector and forecasts the survivors' needs. Case studies using data collected during Hurricane Sandy in 2012, Hurricane Harvey and Hurricane Irma in 2017 demonstrate that the proposed approach outperformed the statistical language model n-gram, LSTM generative model, and convolutional neural network (CNN) based model. This research indicates its great promise for enhancing disaster management such as evacuation planning and commodity delivery.

Original languageEnglish
Pages (from-to)229-240
Number of pages12
JournalIEEE Transactions on Big Data
Issue number1
StatePublished - Feb 1 2022


  • Concern flow
  • Disaster relief
  • Hurricane events
  • LSTM
  • Needs forecasting
  • Sequence to sequence model


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